Hi @himanshu, the simplest way to implement custom loss functions is by subclassing the Trainer
class and overriding the compute_loss
function, e.g.
from transformers import Trainer
class BartTrainer(Trainer):
def compute_loss(self, model, inputs):
# implement custom logic here
custom_loss = ...
return custom_loss
You can find more details in the docs here: Trainer — transformers 4.3.0 documentation